Stop using bivariate correlations for variable selection The real problem comes into play here — the bivariate comparsions selects for the wrong variables by over-emphasizing the relationship between the marginal distributions. Because the selected model is incomplete and important variables are omitted, the resulting parameter estimates are biased and inaccurate. The bivariate comparsion is a terrible way to select relevant variables for a highly dimensional model as the function of interest...

Boring machine learning is where it's at go grab a spreadsheet and figure out how to get the best result on a boring economics problem with a boring algorithm; Don't worry so much about making a painting or movie with GANs, we're already really good at doing that and enjoy doing it. ...

I finally understand Python decorators! def bread(func): def wrapper(): print("</''''''\>") func() print("<\______/>") return wrapper def ingredients(func): def wrapper(): print("#tomatoes#") func() print("~salad~") return wrapper def sandwich(food="--ham--"): print(food) Without a decorator sandwich() #outputs: --ham-- sandwich = bread(ingredients(sandwich)) sandwich() #outputs: #</''''''\> # #tomatoes# # -...